Overview

Dataset statistics

Number of variables16
Number of observations90642
Missing cells292
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory128.0 B

Variable types

Categorical7
Numeric9

Alerts

province has a high cardinality: 90 distinct values High cardinality
municipality has a high cardinality: 1454 distinct values High cardinality
barangay has a high cardinality: 26402 distinct values High cardinality
precincts has a high cardinality: 41457 distinct values High cardinality
polling_center has a high cardinality: 28639 distinct values High cardinality
timestamp has a high cardinality: 29598 distinct values High cardinality
registered_voters is highly correlated with ballots_castHigh correlation
ballots_cast is highly correlated with registered_votersHigh correlation
registered_voters is highly correlated with ballots_castHigh correlation
ballots_cast is highly correlated with registered_votersHigh correlation
registered_voters is highly correlated with ballots_castHigh correlation
ballots_cast is highly correlated with registered_votersHigh correlation
region is highly correlated with provinceHigh correlation
province is highly correlated with regionHigh correlation
region is highly correlated with province and 6 other fieldsHigh correlation
province is highly correlated with region and 7 other fieldsHigh correlation
clustered_precinct is highly correlated with region and 4 other fieldsHigh correlation
cayetano is highly correlated with region and 2 other fieldsHigh correlation
escudero is highly correlated with provinceHigh correlation
marcos is highly correlated with region and 2 other fieldsHigh correlation
robredo is highly correlated with region and 2 other fieldsHigh correlation
registered_voters is highly correlated with region and 3 other fieldsHigh correlation
ballots_cast is highly correlated with region and 3 other fieldsHigh correlation
clustered_precinct has unique values Unique
cayetano has 1078 (1.2%) zeros Zeros
honasan has 3284 (3.6%) zeros Zeros
trillanes has 4286 (4.7%) zeros Zeros

Reproduction

Analysis started2022-05-24 11:22:00.758961
Analysis finished2022-05-24 11:22:20.939929
Duration20.18 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size708.3 KiB
REGION IV-A
12034 
REGION III
9504 
NCR
9234 
REGION VIII
5705 
REGION VII
5700 
Other values (14)
48465 

Length

Max length11
Median length9
Mean length8.35064319
Min length3

Characters and Unicode

Total characters756919
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREGION I
2nd rowREGION I
3rd rowREGION I
4th rowREGION I
5th rowREGION I

Common Values

ValueCountFrequency (%)
REGION IV-A12034
13.3%
REGION III9504
 
10.5%
NCR9234
 
10.2%
REGION VIII5705
 
6.3%
REGION VII5700
 
6.3%
REGION V5554
 
6.1%
REGION I5300
 
5.8%
REGION VI5016
 
5.5%
REGION X4207
 
4.6%
REGION XI3942
 
4.3%
Other values (9)24446
27.0%

Length

2022-05-24T19:22:21.019920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region72134
44.3%
iv-a12034
 
7.4%
iii9504
 
5.8%
ncr9234
 
5.7%
viii5705
 
3.5%
vii5700
 
3.5%
v5554
 
3.4%
i5300
 
3.3%
vi5016
 
3.1%
x4207
 
2.6%
Other values (10)28388
 
17.4%

Most occurring characters

ValueCountFrequency (%)
I185788
24.5%
R90159
11.9%
N85138
11.2%
O72617
 
9.6%
E72134
 
9.5%
G72134
 
9.5%
72134
 
9.5%
V37250
 
4.9%
A17538
 
2.3%
X17105
 
2.3%
Other values (4)34922
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter669993
88.5%
Space Separator72134
 
9.5%
Dash Punctuation14792
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I185788
27.7%
R90159
13.5%
N85138
12.7%
O72617
 
10.8%
E72134
 
10.8%
G72134
 
10.8%
V37250
 
5.6%
A17538
 
2.6%
X17105
 
2.6%
C11138
 
1.7%
Other values (2)8992
 
1.3%
Space Separator
ValueCountFrequency (%)
72134
100.0%
Dash Punctuation
ValueCountFrequency (%)
-14792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669993
88.5%
Common86926
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
I185788
27.7%
R90159
13.5%
N85138
12.7%
O72617
 
10.8%
E72134
 
10.8%
G72134
 
10.8%
V37250
 
5.6%
A17538
 
2.6%
X17105
 
2.6%
C11138
 
1.7%
Other values (2)8992
 
1.3%
Common
ValueCountFrequency (%)
72134
83.0%
-14792
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII756919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I185788
24.5%
R90159
11.9%
N85138
11.2%
O72617
 
9.6%
E72134
 
9.5%
G72134
 
9.5%
72134
 
9.5%
V37250
 
4.9%
A17538
 
2.3%
X17105
 
2.3%
Other values (4)34922
 
4.6%

province
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size708.3 KiB
CEBU
 
4021
NATIONAL CAPITAL REGION - SECOND DISTRICT
 
2879
CAVITE
 
2795
PANGASINAN
 
2777
ILOILO
 
2726
Other values (85)
75444 

Length

Max length41
Median length21
Mean length12.79070409
Min length4

Characters and Unicode

Total characters1159375
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPANGASINAN
2nd rowPANGASINAN
3rd rowPANGASINAN
4th rowPANGASINAN
5th rowPANGASINAN

Common Values

ValueCountFrequency (%)
CEBU4021
 
4.4%
NATIONAL CAPITAL REGION - SECOND DISTRICT2879
 
3.2%
CAVITE2795
 
3.1%
PANGASINAN2777
 
3.1%
ILOILO2726
 
3.0%
LAGUNA2552
 
2.8%
BATANGAS2495
 
2.8%
NEGROS OCCIDENTAL2493
 
2.8%
BULACAN2435
 
2.7%
LEYTE2288
 
2.5%
Other values (80)63181
69.7%

Length

2022-05-24T19:22:21.127763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
del9317
 
5.2%
9234
 
5.2%
national8656
 
4.9%
capital8656
 
4.9%
region8656
 
4.9%
sur8263
 
4.6%
district6996
 
3.9%
norte5063
 
2.8%
davao4070
 
2.3%
cebu4021
 
2.3%
Other values (92)105288
59.1%

Most occurring characters

ValueCountFrequency (%)
A172873
14.9%
N101157
 
8.7%
I89331
 
7.7%
87578
 
7.6%
O83677
 
7.2%
T76244
 
6.6%
E72556
 
6.3%
L68199
 
5.9%
R62462
 
5.4%
S54719
 
4.7%
Other values (20)290579
25.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1054913
91.0%
Space Separator87578
 
7.6%
Dash Punctuation9574
 
0.8%
Open Punctuation3079
 
0.3%
Close Punctuation3079
 
0.3%
Other Punctuation1152
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A172873
16.4%
N101157
9.6%
I89331
 
8.5%
O83677
 
7.9%
T76244
 
7.2%
E72556
 
6.9%
L68199
 
6.5%
R62462
 
5.9%
S54719
 
5.2%
C49935
 
4.7%
Other values (15)223760
21.2%
Space Separator
ValueCountFrequency (%)
87578
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9574
100.0%
Open Punctuation
ValueCountFrequency (%)
(3079
100.0%
Close Punctuation
ValueCountFrequency (%)
)3079
100.0%
Other Punctuation
ValueCountFrequency (%)
.1152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1054913
91.0%
Common104462
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A172873
16.4%
N101157
9.6%
I89331
 
8.5%
O83677
 
7.9%
T76244
 
7.2%
E72556
 
6.9%
L68199
 
6.5%
R62462
 
5.9%
S54719
 
5.2%
C49935
 
4.7%
Other values (15)223760
21.2%
Common
ValueCountFrequency (%)
87578
83.8%
-9574
 
9.2%
(3079
 
2.9%
)3079
 
2.9%
.1152
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1159375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A172873
14.9%
N101157
 
8.7%
I89331
 
7.7%
87578
 
7.6%
O83677
 
7.2%
T76244
 
6.6%
E72556
 
6.3%
L68199
 
5.9%
R62462
 
5.4%
S54719
 
4.7%
Other values (20)290579
25.1%

municipality
Categorical

HIGH CARDINALITY

Distinct1454
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size708.3 KiB
QUEZON CITY
 
1651
DAVAO CITY
 
1279
CALOOCAN CITY
 
957
CEBU CITY
 
836
CITY OF ANTIPOLO
 
638
Other values (1449)
85281 

Length

Max length47
Median length30
Mean length9.934765341
Min length3

Characters and Unicode

Total characters900507
Distinct characters34
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCALASIAO
2nd rowCALASIAO
3rd rowCALASIAO
4th rowCALASIAO
5th rowCALASIAO

Common Values

ValueCountFrequency (%)
QUEZON CITY1651
 
1.8%
DAVAO CITY1279
 
1.4%
CALOOCAN CITY957
 
1.1%
CEBU CITY836
 
0.9%
CITY OF ANTIPOLO638
 
0.7%
ZAMBOANGA CITY616
 
0.7%
TONDO557
 
0.6%
CITY OF MAKATI556
 
0.6%
SANTA CRUZ545
 
0.6%
CITY OF PASIG533
 
0.6%
Other values (1444)82474
91.0%

Length

2022-05-24T19:22:21.235804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city28936
 
19.7%
of6087
 
4.2%
san5751
 
3.9%
santa2181
 
1.5%
quezon1876
 
1.3%
davao1279
 
0.9%
caloocan957
 
0.7%
jose934
 
0.6%
cebu836
 
0.6%
general785
 
0.5%
Other values (1530)96996
66.2%

Most occurring characters

ValueCountFrequency (%)
A155763
17.3%
I73815
 
8.2%
N71693
 
8.0%
O62985
 
7.0%
T58241
 
6.5%
57936
 
6.4%
C52322
 
5.8%
L43156
 
4.8%
Y39979
 
4.4%
S35465
 
3.9%
Other values (24)249152
27.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter834110
92.6%
Space Separator57936
 
6.4%
Open Punctuation3330
 
0.4%
Close Punctuation3330
 
0.4%
Dash Punctuation975
 
0.1%
Other Punctuation754
 
0.1%
Modifier Symbol72
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A155763
18.7%
I73815
 
8.8%
N71693
 
8.6%
O62985
 
7.6%
T58241
 
7.0%
C52322
 
6.3%
L43156
 
5.2%
Y39979
 
4.8%
S35465
 
4.3%
G30466
 
3.7%
Other values (17)210225
25.2%
Other Punctuation
ValueCountFrequency (%)
.612
81.2%
'142
 
18.8%
Space Separator
ValueCountFrequency (%)
57936
100.0%
Open Punctuation
ValueCountFrequency (%)
(3330
100.0%
Close Punctuation
ValueCountFrequency (%)
)3330
100.0%
Dash Punctuation
ValueCountFrequency (%)
-975
100.0%
Modifier Symbol
ValueCountFrequency (%)
`72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin834110
92.6%
Common66397
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A155763
18.7%
I73815
 
8.8%
N71693
 
8.6%
O62985
 
7.6%
T58241
 
7.0%
C52322
 
6.3%
L43156
 
5.2%
Y39979
 
4.8%
S35465
 
4.3%
G30466
 
3.7%
Other values (17)210225
25.2%
Common
ValueCountFrequency (%)
57936
87.3%
(3330
 
5.0%
)3330
 
5.0%
-975
 
1.5%
.612
 
0.9%
'142
 
0.2%
`72
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII898329
99.8%
None2178
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A155763
17.3%
I73815
 
8.2%
N71693
 
8.0%
O62985
 
7.0%
T58241
 
6.5%
57936
 
6.4%
C52322
 
5.8%
L43156
 
4.8%
Y39979
 
4.5%
S35465
 
3.9%
Other values (23)246974
27.5%
None
ValueCountFrequency (%)
Ñ2178
100.0%

barangay
Categorical

HIGH CARDINALITY

Distinct26402
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size708.3 KiB
POBLACION
 
2276
SAN ISIDRO
 
762
SAN JOSE
 
548
SAN ROQUE
 
435
SAN ANTONIO
 
417
Other values (26397)
86204 

Length

Max length54
Median length44
Mean length10.00989607
Min length2

Characters and Unicode

Total characters907317
Distinct characters46
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11731 ?
Unique (%)12.9%

Sample

1st rowBUENLAG
2nd rowBUENLAG
3rd rowBUENLAG
4th rowBUENLAG
5th rowBUENLAG

Common Values

ValueCountFrequency (%)
POBLACION2276
 
2.5%
SAN ISIDRO762
 
0.8%
SAN JOSE548
 
0.6%
SAN ROQUE435
 
0.5%
SAN ANTONIO417
 
0.5%
SAN JUAN398
 
0.4%
SAN VICENTE396
 
0.4%
SANTO NIÑO357
 
0.4%
SANTA CRUZ320
 
0.4%
SAN MIGUEL251
 
0.3%
Other values (26392)84482
93.2%

Length

2022-05-24T19:22:21.351772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san7536
 
5.4%
pob7019
 
5.0%
barangay5614
 
4.0%
poblacion4223
 
3.0%
santa1846
 
1.3%
i1107
 
0.8%
santo1093
 
0.8%
ii1084
 
0.8%
isidro1002
 
0.7%
jose942
 
0.7%
Other values (21702)108888
77.6%

Most occurring characters

ValueCountFrequency (%)
A166511
18.4%
N88980
 
9.8%
O65970
 
7.3%
I55326
 
6.1%
52192
 
5.8%
B45124
 
5.0%
L43102
 
4.8%
G40869
 
4.5%
S38313
 
4.2%
T32779
 
3.6%
Other values (36)278151
30.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter808429
89.1%
Space Separator52192
 
5.8%
Decimal Number13307
 
1.5%
Close Punctuation9863
 
1.1%
Open Punctuation9863
 
1.1%
Other Punctuation9442
 
1.0%
Dash Punctuation4208
 
0.5%
Connector Punctuation13
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A166511
20.6%
N88980
11.0%
O65970
 
8.2%
I55326
 
6.8%
B45124
 
5.6%
L43102
 
5.3%
G40869
 
5.1%
S38313
 
4.7%
T32779
 
4.1%
R30975
 
3.8%
Other values (17)200480
24.8%
Decimal Number
ValueCountFrequency (%)
12808
21.1%
21645
12.4%
31374
10.3%
71311
9.9%
61250
9.4%
41174
8.8%
51132
8.5%
81126
8.5%
9781
 
5.9%
0706
 
5.3%
Other Punctuation
ValueCountFrequency (%)
.9096
96.3%
,285
 
3.0%
'42
 
0.4%
*19
 
0.2%
Space Separator
ValueCountFrequency (%)
52192
100.0%
Close Punctuation
ValueCountFrequency (%)
)9863
100.0%
Open Punctuation
ValueCountFrequency (%)
(9863
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4208
100.0%
Connector Punctuation
ValueCountFrequency (%)
_13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin808429
89.1%
Common98888
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A166511
20.6%
N88980
11.0%
O65970
 
8.2%
I55326
 
6.8%
B45124
 
5.6%
L43102
 
5.3%
G40869
 
5.1%
S38313
 
4.7%
T32779
 
4.1%
R30975
 
3.8%
Other values (17)200480
24.8%
Common
ValueCountFrequency (%)
52192
52.8%
)9863
 
10.0%
(9863
 
10.0%
.9096
 
9.2%
-4208
 
4.3%
12808
 
2.8%
21645
 
1.7%
31374
 
1.4%
71311
 
1.3%
61250
 
1.3%
Other values (9)5278
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII906374
99.9%
None943
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A166511
18.4%
N88980
 
9.8%
O65970
 
7.3%
I55326
 
6.1%
52192
 
5.8%
B45124
 
5.0%
L43102
 
4.8%
G40869
 
4.5%
S38313
 
4.2%
T32779
 
3.6%
Other values (35)277208
30.6%
None
ValueCountFrequency (%)
Ñ943
100.0%

clustered_precinct
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct90642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41417585.32
Minimum1010001
Maximum93150065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:21.471806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1010001
5-th percentile7100014.05
Q122170553.25
median39101196.5
Q358080184.75
95-th percentile76040242.95
Maximum93150065
Range92140064
Interquartile range (IQR)35909631.5

Descriptive statistics

Standard deviation22649418.45
Coefficient of variation (CV)0.5468551167
Kurtosis-1.089332949
Mean41417585.32
Median Absolute Deviation (MAD)17901146
Skewness0.1356742081
Sum3.754172768 × 1012
Variance5.129961563 × 1014
MonotonicityNot monotonic
2022-05-24T19:22:21.589808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551700261
 
< 0.1%
821100581
 
< 0.1%
821100211
 
< 0.1%
821100181
 
< 0.1%
821100061
 
< 0.1%
821100261
 
< 0.1%
821100271
 
< 0.1%
821100251
 
< 0.1%
821100671
 
< 0.1%
821100661
 
< 0.1%
Other values (90632)90632
> 99.9%
ValueCountFrequency (%)
10100011
< 0.1%
10100021
< 0.1%
10100031
< 0.1%
10100041
< 0.1%
10100051
< 0.1%
10100061
< 0.1%
10100071
< 0.1%
10100081
< 0.1%
10100091
< 0.1%
10100101
< 0.1%
ValueCountFrequency (%)
931500651
< 0.1%
931500641
< 0.1%
931500631
< 0.1%
931500621
< 0.1%
931500611
< 0.1%
931500601
< 0.1%
931500591
< 0.1%
931500581
< 0.1%
931500571
< 0.1%
931500561
< 0.1%

cayetano
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct496
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.64745924
Minimum0
Maximum612
Zeros1078
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:21.722771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median40
Q372
95-th percentile223
Maximum612
Range612
Interquartile range (IQR)54

Descriptive statistics

Standard deviation72.24853121
Coefficient of variation (CV)1.153255568
Kurtosis6.242696969
Mean62.64745924
Median Absolute Deviation (MAD)25
Skewness2.347674801
Sum5678491
Variance5219.850262
MonotonicityNot monotonic
2022-05-24T19:22:21.837773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91393
 
1.5%
81370
 
1.5%
71339
 
1.5%
111320
 
1.5%
101318
 
1.5%
61311
 
1.4%
51308
 
1.4%
141290
 
1.4%
131281
 
1.4%
121271
 
1.4%
Other values (486)77441
85.4%
ValueCountFrequency (%)
01078
1.2%
11043
1.2%
21169
1.3%
31250
1.4%
41267
1.4%
51308
1.4%
61311
1.4%
71339
1.5%
81370
1.5%
91393
1.5%
ValueCountFrequency (%)
6121
< 0.1%
5391
< 0.1%
5321
< 0.1%
5141
< 0.1%
5131
< 0.1%
5062
< 0.1%
5051
< 0.1%
5041
< 0.1%
5021
< 0.1%
4971
< 0.1%

escudero
Real number (ℝ≥0)

HIGH CORRELATION

Distinct450
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.09176761
Minimum0
Maximum692
Zeros853
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:21.958773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q125
median44
Q372
95-th percentile123
Maximum692
Range692
Interquartile range (IQR)47

Descriptive statistics

Standard deviation42.24989459
Coefficient of variation (CV)0.7957899404
Kurtosis15.74278833
Mean53.09176761
Median Absolute Deviation (MAD)23
Skewness2.629427762
Sum4812344
Variance1785.053592
MonotonicityNot monotonic
2022-05-24T19:22:22.071772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321255
 
1.4%
381196
 
1.3%
301189
 
1.3%
351185
 
1.3%
291181
 
1.3%
261168
 
1.3%
281165
 
1.3%
361163
 
1.3%
331158
 
1.3%
341146
 
1.3%
Other values (440)78836
87.0%
ValueCountFrequency (%)
0853
0.9%
1638
0.7%
2683
0.8%
3708
0.8%
4720
0.8%
5706
0.8%
6729
0.8%
7773
0.9%
8753
0.8%
9853
0.9%
ValueCountFrequency (%)
6921
< 0.1%
6761
< 0.1%
6601
< 0.1%
5531
< 0.1%
5521
< 0.1%
5392
< 0.1%
5371
< 0.1%
5181
< 0.1%
5161
< 0.1%
5141
< 0.1%

honasan
Real number (ℝ≥0)

ZEROS

Distinct204
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.389510382
Minimum0
Maximum734
Zeros3284
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:22.193805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q310
95-th percentile22
Maximum734
Range734
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.54994631
Coefficient of variation (CV)1.495909265
Kurtosis651.8370312
Mean8.389510382
Median Absolute Deviation (MAD)3
Skewness18.83783216
Sum760442
Variance157.5011523
MonotonicityNot monotonic
2022-05-24T19:22:22.308805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48486
 
9.4%
58307
 
9.2%
38197
 
9.0%
67689
 
8.5%
27074
 
7.8%
76598
 
7.3%
85798
 
6.4%
15338
 
5.9%
94789
 
5.3%
104032
 
4.4%
Other values (194)24334
26.8%
ValueCountFrequency (%)
03284
 
3.6%
15338
5.9%
27074
7.8%
38197
9.0%
48486
9.4%
58307
9.2%
67689
8.5%
76598
7.3%
85798
6.4%
94789
5.3%
ValueCountFrequency (%)
7341
< 0.1%
5651
< 0.1%
5311
< 0.1%
5281
< 0.1%
5181
< 0.1%
5171
< 0.1%
5061
< 0.1%
5021
< 0.1%
5011
< 0.1%
5001
< 0.1%

marcos
Real number (ℝ≥0)

HIGH CORRELATION

Distinct668
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.3011628
Minimum0
Maximum756
Zeros374
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:22.431773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q160
median129
Q3225
95-th percentile363
Maximum756
Range756
Interquartile range (IQR)165

Descriptive statistics

Standard deviation112.8127294
Coefficient of variation (CV)0.7407213926
Kurtosis0.8069760853
Mean152.3011628
Median Absolute Deviation (MAD)79
Skewness0.9475807224
Sum13804882
Variance12726.71192
MonotonicityNot monotonic
2022-05-24T19:22:22.542808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26463
 
0.5%
31454
 
0.5%
43453
 
0.5%
18444
 
0.5%
17434
 
0.5%
22431
 
0.5%
21431
 
0.5%
29429
 
0.5%
45423
 
0.5%
28423
 
0.5%
Other values (658)86257
95.2%
ValueCountFrequency (%)
0374
0.4%
1126
 
0.1%
2165
0.2%
3190
0.2%
4218
0.2%
5253
0.3%
6280
0.3%
7291
0.3%
8324
0.4%
9309
0.3%
ValueCountFrequency (%)
7561
< 0.1%
7531
< 0.1%
7491
< 0.1%
7301
< 0.1%
7232
< 0.1%
7141
< 0.1%
7111
< 0.1%
6981
< 0.1%
6951
< 0.1%
6941
< 0.1%

robredo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct629
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.712385
Minimum0
Maximum754
Zeros420
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:22.662271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q187
median144
Q3204
95-th percentile339
Maximum754
Range754
Interquartile range (IQR)117

Descriptive statistics

Standard deviation95.99530347
Coefficient of variation (CV)0.6204758816
Kurtosis1.344355273
Mean154.712385
Median Absolute Deviation (MAD)58
Skewness0.9255990239
Sum14023440
Variance9215.098287
MonotonicityNot monotonic
2022-05-24T19:22:22.776310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138483
 
0.5%
154477
 
0.5%
156470
 
0.5%
150467
 
0.5%
147467
 
0.5%
139466
 
0.5%
127464
 
0.5%
137458
 
0.5%
124457
 
0.5%
132457
 
0.5%
Other values (619)85976
94.9%
ValueCountFrequency (%)
0420
0.5%
1226
0.2%
2213
0.2%
3215
0.2%
4217
0.2%
5209
0.2%
6197
0.2%
7205
0.2%
8196
0.2%
9194
0.2%
ValueCountFrequency (%)
7541
< 0.1%
7511
< 0.1%
7431
< 0.1%
7321
< 0.1%
7161
< 0.1%
7081
< 0.1%
7061
< 0.1%
7051
< 0.1%
7031
< 0.1%
6981
< 0.1%

trillanes
Real number (ℝ≥0)

ZEROS

Distinct150
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.313828027
Minimum0
Maximum344
Zeros4286
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:23.085287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q312
95-th percentile25
Maximum344
Range344
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.416564774
Coefficient of variation (CV)1.011030561
Kurtosis58.68963848
Mean9.313828027
Median Absolute Deviation (MAD)4
Skewness4.603380055
Sum844224
Variance88.67169215
MonotonicityNot monotonic
2022-05-24T19:22:23.200309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56656
 
7.3%
66650
 
7.3%
46645
 
7.3%
36631
 
7.3%
76017
 
6.6%
25973
 
6.6%
85470
 
6.0%
15105
 
5.6%
94817
 
5.3%
04286
 
4.7%
Other values (140)32392
35.7%
ValueCountFrequency (%)
04286
4.7%
15105
5.6%
25973
6.6%
36631
7.3%
46645
7.3%
56656
7.3%
66650
7.3%
76017
6.6%
85470
6.0%
94817
5.3%
ValueCountFrequency (%)
3441
< 0.1%
2411
< 0.1%
2301
< 0.1%
2181
< 0.1%
1891
< 0.1%
1851
< 0.1%
1831
< 0.1%
1791
< 0.1%
1751
< 0.1%
1601
< 0.1%

registered_voters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct771
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591.695031
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:23.325283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile279
Q1494
median619
Q3722
95-th percentile787
Maximum1000
Range999
Interquartile range (IQR)228

Descriptive statistics

Standard deviation160.0241137
Coefficient of variation (CV)0.2704503255
Kurtosis0.1221898841
Mean591.695031
Median Absolute Deviation (MAD)111
Skewness-0.7190745318
Sum53632421
Variance25607.71698
MonotonicityNot monotonic
2022-05-24T19:22:23.443281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800567
 
0.6%
1000433
 
0.5%
759327
 
0.4%
778326
 
0.4%
773324
 
0.4%
798324
 
0.4%
784319
 
0.4%
757316
 
0.3%
779316
 
0.3%
765311
 
0.3%
Other values (761)87079
96.1%
ValueCountFrequency (%)
11
 
< 0.1%
23
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
241
 
< 0.1%
261
 
< 0.1%
332
< 0.1%
371
 
< 0.1%
411
 
< 0.1%
ValueCountFrequency (%)
1000433
0.5%
95018
 
< 0.1%
9001
 
< 0.1%
8991
 
< 0.1%
8511
 
< 0.1%
8481
 
< 0.1%
8431
 
< 0.1%
8091
 
< 0.1%
800567
0.6%
799306
0.3%

ballots_cast
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct793
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.3135302
Minimum0
Maximum1000
Zeros241
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size708.3 KiB
2022-05-24T19:22:23.561279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229
Q1403
median501
Q3583
95-th percentile658
Maximum1000
Range1000
Interquartile range (IQR)180

Descriptive statistics

Standard deviation132.6187508
Coefficient of variation (CV)0.2749637788
Kurtosis0.515933745
Mean482.3135302
Median Absolute Deviation (MAD)88
Skewness-0.6544986463
Sum43717863
Variance17587.73307
MonotonicityNot monotonic
2022-05-24T19:22:23.674281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
592324
 
0.4%
563314
 
0.3%
517313
 
0.3%
575307
 
0.3%
595304
 
0.3%
578304
 
0.3%
556304
 
0.3%
510302
 
0.3%
554301
 
0.3%
530301
 
0.3%
Other values (783)87568
96.6%
ValueCountFrequency (%)
0241
0.3%
41
 
< 0.1%
121
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
181
 
< 0.1%
201
 
< 0.1%
231
 
< 0.1%
241
 
< 0.1%
292
 
< 0.1%
ValueCountFrequency (%)
100081
0.1%
99922
 
< 0.1%
9981
 
< 0.1%
9976
 
< 0.1%
9961
 
< 0.1%
9931
 
< 0.1%
9921
 
< 0.1%
9901
 
< 0.1%
9892
 
< 0.1%
9831
 
< 0.1%

precincts
Categorical

HIGH CARDINALITY

Distinct41457
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size708.3 KiB
0001A, 0001B, 0002A, 0002B
 
208
0003A, 0003B, 0004A, 0004B
 
108
0023A, 0023B, 0024A, 0024B
 
102
0001A, 0002A, 0003A, 0004A
 
101
0020A, 0020B, 0021A, 0021B
 
98
Other values (41452)
90025 

Length

Max length56
Median length55
Mean length25.92500165
Min length5

Characters and Unicode

Total characters2349894
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32999 ?
Unique (%)36.4%

Sample

1st row0060A, 0060B, 0061A, 0062A
2nd row0062B, 0063A, 0063B, 0064A
3rd row0051C, 0052A, 0052B, 0053A, 0053B
4th row0049A, 0050A, 0050B, 0051A, 0051B
5th row0056C, 0057A, 0057B, 0058A, 0058B, 0059A, 0059B

Common Values

ValueCountFrequency (%)
0001A, 0001B, 0002A, 0002B208
 
0.2%
0003A, 0003B, 0004A, 0004B108
 
0.1%
0023A, 0023B, 0024A, 0024B102
 
0.1%
0001A, 0002A, 0003A, 0004A101
 
0.1%
0020A, 0020B, 0021A, 0021B98
 
0.1%
0024A, 0024B, 0025A, 0025B92
 
0.1%
0012A, 0012B, 0013A, 0013B92
 
0.1%
0001A, 0001B, 0002A, 0003A91
 
0.1%
0025A, 0025B, 0026A, 0026B90
 
0.1%
0010A, 0010B, 0011A, 0011B89
 
0.1%
Other values (41447)89571
98.8%

Length

2022-05-24T19:22:23.792506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0001a1590
 
0.4%
0002a1577
 
0.4%
0003a1571
 
0.4%
0004a1569
 
0.4%
0005a1566
 
0.4%
0009a1560
 
0.4%
0006a1560
 
0.4%
0007a1557
 
0.4%
0010a1555
 
0.4%
0008a1553
 
0.4%
Other values (21716)340223
95.6%

Most occurring characters

ValueCountFrequency (%)
0618335
26.3%
,270871
11.5%
265239
11.3%
A210539
 
9.0%
1170291
 
7.2%
2117182
 
5.0%
B109390
 
4.7%
3100201
 
4.3%
488901
 
3.8%
582120
 
3.5%
Other values (30)316825
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1452173
61.8%
Uppercase Letter361611
 
15.4%
Other Punctuation270871
 
11.5%
Space Separator265239
 
11.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A210539
58.2%
B109390
30.3%
C20010
 
5.5%
D6490
 
1.8%
P6256
 
1.7%
E2935
 
0.8%
F1697
 
0.5%
G1086
 
0.3%
H744
 
0.2%
I517
 
0.1%
Other values (18)1947
 
0.5%
Decimal Number
ValueCountFrequency (%)
0618335
42.6%
1170291
 
11.7%
2117182
 
8.1%
3100201
 
6.9%
488901
 
6.1%
582120
 
5.7%
676087
 
5.2%
770292
 
4.8%
865922
 
4.5%
962842
 
4.3%
Other Punctuation
ValueCountFrequency (%)
,270871
100.0%
Space Separator
ValueCountFrequency (%)
265239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1988283
84.6%
Latin361611
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A210539
58.2%
B109390
30.3%
C20010
 
5.5%
D6490
 
1.8%
P6256
 
1.7%
E2935
 
0.8%
F1697
 
0.5%
G1086
 
0.3%
H744
 
0.2%
I517
 
0.1%
Other values (18)1947
 
0.5%
Common
ValueCountFrequency (%)
0618335
31.1%
,270871
13.6%
265239
13.3%
1170291
 
8.6%
2117182
 
5.9%
3100201
 
5.0%
488901
 
4.5%
582120
 
4.1%
676087
 
3.8%
770292
 
3.5%
Other values (2)128764
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2349889
> 99.9%
None5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0618335
26.3%
,270871
11.5%
265239
11.3%
A210539
 
9.0%
1170291
 
7.2%
2117182
 
5.0%
B109390
 
4.7%
3100201
 
4.3%
488901
 
3.8%
582120
 
3.5%
Other values (28)316820
13.5%
None
ValueCountFrequency (%)
À4
80.0%
È1
 
20.0%

polling_center
Categorical

HIGH CARDINALITY

Distinct28639
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size708.3 KiB
SAN ISIDRO ELEMENTARY SCHOOL
 
389
BARANGAY HALL
 
296
SAN JOSE ELEMENTARY SCHOOL
 
277
SAN ROQUE ELEMENTARY SCHOOL
 
271
SAN ANTONIO ELEMENTARY SCHOOL
 
242
Other values (28634)
89167 

Length

Max length84
Median length76
Mean length26.61664571
Min length3

Characters and Unicode

Total characters2412586
Distinct characters46
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12744 ?
Unique (%)14.1%

Sample

1st rowBUENLAG ELEMENTARY SCHOOL
2nd rowBUENLAG ELEMENTARY SCHOOL
3rd rowBUENLAG ELEMENTARY SCHOOL
4th rowBUENLAG ELEMENTARY SCHOOL
5th rowBUENLAG ELEMENTARY SCHOOL

Common Values

ValueCountFrequency (%)
SAN ISIDRO ELEMENTARY SCHOOL389
 
0.4%
BARANGAY HALL296
 
0.3%
SAN JOSE ELEMENTARY SCHOOL277
 
0.3%
SAN ROQUE ELEMENTARY SCHOOL271
 
0.3%
SAN ANTONIO ELEMENTARY SCHOOL242
 
0.3%
SAN VICENTE ELEMENTARY SCHOOL237
 
0.3%
RIZAL ELEMENTARY SCHOOL201
 
0.2%
SAN JUAN ELEMENTARY SCHOOL192
 
0.2%
SAN MIGUEL ELEMENTARY SCHOOL188
 
0.2%
SAN FRANCISCO ELEMENTARY SCHOOL144
 
0.2%
Other values (28629)88205
97.3%

Length

2022-05-24T19:22:23.922550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
school82237
25.5%
elementary56344
 
17.4%
elem12686
 
3.9%
central9070
 
2.8%
san5990
 
1.9%
high4013
 
1.2%
primary3667
 
1.1%
hall2636
 
0.8%
national2385
 
0.7%
barangay2366
 
0.7%
Other values (21046)141660
43.9%

Most occurring characters

ValueCountFrequency (%)
E247549
10.3%
A245279
10.2%
234555
 
9.7%
O232636
 
9.6%
L221114
 
9.2%
N158832
 
6.6%
S129417
 
5.4%
C123822
 
5.1%
R113631
 
4.7%
T109505
 
4.5%
Other values (36)596246
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2145484
88.9%
Space Separator234555
 
9.7%
Other Punctuation24867
 
1.0%
Dash Punctuation3977
 
0.2%
Open Punctuation1315
 
0.1%
Close Punctuation1299
 
0.1%
Decimal Number1079
 
< 0.1%
Modifier Symbol10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E247549
11.5%
A245279
11.4%
O232636
10.8%
L221114
10.3%
N158832
 
7.4%
S129417
 
6.0%
C123822
 
5.8%
R113631
 
5.3%
T109505
 
5.1%
M104898
 
4.9%
Other values (17)458801
21.4%
Decimal Number
ValueCountFrequency (%)
1424
39.3%
2258
23.9%
3142
 
13.2%
858
 
5.4%
656
 
5.2%
554
 
5.0%
431
 
2.9%
725
 
2.3%
019
 
1.8%
912
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.22663
91.1%
,1631
 
6.6%
/429
 
1.7%
&144
 
0.6%
Space Separator
ValueCountFrequency (%)
234555
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3977
100.0%
Open Punctuation
ValueCountFrequency (%)
(1315
100.0%
Close Punctuation
ValueCountFrequency (%)
)1299
100.0%
Modifier Symbol
ValueCountFrequency (%)
`10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2145484
88.9%
Common267102
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E247549
11.5%
A245279
11.4%
O232636
10.8%
L221114
10.3%
N158832
 
7.4%
S129417
 
6.0%
C123822
 
5.8%
R113631
 
5.3%
T109505
 
5.1%
M104898
 
4.9%
Other values (17)458801
21.4%
Common
ValueCountFrequency (%)
234555
87.8%
.22663
 
8.5%
-3977
 
1.5%
,1631
 
0.6%
(1315
 
0.5%
)1299
 
0.5%
/429
 
0.2%
1424
 
0.2%
2258
 
0.1%
&144
 
0.1%
Other values (9)407
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2411013
99.9%
None1573
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E247549
10.3%
A245279
10.2%
234555
 
9.7%
O232636
 
9.6%
L221114
 
9.2%
N158832
 
6.6%
S129417
 
5.4%
C123822
 
5.1%
R113631
 
4.7%
T109505
 
4.5%
Other values (35)594673
24.7%
None
ValueCountFrequency (%)
Ñ1573
100.0%

timestamp
Categorical

HIGH CARDINALITY

Distinct29598
Distinct (%)32.8%
Missing292
Missing (%)0.3%
Memory size708.3 KiB
05/09/2016 18:32:18
 
88
05/09/2016 18:32:30
 
80
05/09/2016 18:33:05
 
77
05/09/2016 18:32:15
 
75
05/09/2016 18:33:29
 
67
Other values (29593)
89963 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1716650
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15902 ?
Unique (%)17.6%

Sample

1st row05/09/2016 19:11:37
2nd row05/09/2016 18:39:48
3rd row05/09/2016 18:36:49
4th row05/09/2016 17:12:03
5th row05/09/2016 18:34:08

Common Values

ValueCountFrequency (%)
05/09/2016 18:32:1888
 
0.1%
05/09/2016 18:32:3080
 
0.1%
05/09/2016 18:33:0577
 
0.1%
05/09/2016 18:32:1575
 
0.1%
05/09/2016 18:33:2967
 
0.1%
05/09/2016 18:33:3665
 
0.1%
05/09/2016 18:32:2961
 
0.1%
05/09/2016 18:34:0860
 
0.1%
05/09/2016 18:33:3860
 
0.1%
05/09/2016 17:32:3359
 
0.1%
Other values (29588)89658
98.9%
(Missing)292
 
0.3%

Length

2022-05-24T19:22:24.034517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05/09/201676510
42.3%
05/10/201612692
 
7.0%
05/11/2016965
 
0.5%
05/12/2016183
 
0.1%
18:32:1888
 
< 0.1%
18:32:3080
 
< 0.1%
18:33:0577
 
< 0.1%
18:32:1575
 
< 0.1%
18:33:2967
 
< 0.1%
18:33:3665
 
< 0.1%
Other values (28339)89898
49.7%

Most occurring characters

ValueCountFrequency (%)
0332182
19.4%
1214050
12.5%
/180700
10.5%
:180700
10.5%
2163984
9.6%
5136300
7.9%
9113321
 
6.6%
6108857
 
6.3%
90350
 
5.3%
367554
 
3.9%
Other values (3)128652
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1264900
73.7%
Other Punctuation361400
 
21.1%
Space Separator90350
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0332182
26.3%
1214050
16.9%
2163984
13.0%
5136300
10.8%
9113321
 
9.0%
6108857
 
8.6%
367554
 
5.3%
452297
 
4.1%
849304
 
3.9%
727051
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/180700
50.0%
:180700
50.0%
Space Separator
ValueCountFrequency (%)
90350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1716650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0332182
19.4%
1214050
12.5%
/180700
10.5%
:180700
10.5%
2163984
9.6%
5136300
7.9%
9113321
 
6.6%
6108857
 
6.3%
90350
 
5.3%
367554
 
3.9%
Other values (3)128652
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1716650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0332182
19.4%
1214050
12.5%
/180700
10.5%
:180700
10.5%
2163984
9.6%
5136300
7.9%
9113321
 
6.6%
6108857
 
6.3%
90350
 
5.3%
367554
 
3.9%
Other values (3)128652
 
7.5%

Interactions

2022-05-24T19:22:18.910135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:09.718388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.041049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.123015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.232845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.422028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.505376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.608333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.689139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.025106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:09.914209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.158050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.241051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.347880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.538027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.623332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.724374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.804102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.144135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.088585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.281059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.362018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.465846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.658028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.747367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.843367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.924132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.265137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.213584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.399014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.484014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.585888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.780057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.875367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.964336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:18.043137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.385134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.444292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.519045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.624014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.708856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.901739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.000368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.086368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:18.166136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.513143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.566292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.645014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.751576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.829886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.024773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.122367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.209368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:18.433105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.633138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.684291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.769048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.873878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.066027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.146774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.245336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.332136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:18.554106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.754141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.812387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:11.891049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.999847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.188062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.268773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.369368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.453137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:18.678138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:19.868137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:10.928025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:12.008018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:13.117849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:14.304063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:15.388337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:16.490368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:17.569137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T19:22:18.796137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-24T19:22:24.127551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-24T19:22:24.259516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-24T19:22:24.389548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-24T19:22:24.510515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-24T19:22:24.609549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-24T19:22:20.098012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-24T19:22:20.460004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-24T19:22:20.755918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

regionprovincemunicipalitybarangayclustered_precinctcayetanoescuderohonasanmarcosrobredotrillanesregistered_votersballots_castprecinctspolling_centertimestamp
0REGION IPANGASINANCALASIAOBUENLAG551700262547823013885925030060A, 0060B, 0061A, 0062ABUENLAG ELEMENTARY SCHOOL05/09/2016 19:11:37
1REGION IPANGASINANCALASIAOBUENLAG551700272852319113775264580062B, 0063A, 0063B, 0064ABUENLAG ELEMENTARY SCHOOL05/09/2016 18:39:48
2REGION IPANGASINANCALASIAOBUENLAG5517002332755295156127236490051C, 0052A, 0052B, 0053A, 0053BBUENLAG ELEMENTARY SCHOOL05/09/2016 18:36:49
3REGION IPANGASINANCALASIAOBUENLAG5517002250915291172147686500049A, 0050A, 0050B, 0051A, 0051BBUENLAG ELEMENTARY SCHOOL05/09/2016 17:12:03
4REGION IPANGASINANCALASIAOBUENLAG5517002558963303162147876620056C, 0057A, 0057B, 0058A, 0058B, 0059A, 0059BBUENLAG ELEMENTARY SCHOOL05/09/2016 18:34:08
5REGION IPANGASINANCALASIAOBUENLAG5517002434645290195227466470054A, 0054B, 0055A, 0056A, 0056BBUENLAG ELEMENTARY SCHOOL05/09/2016 19:59:04
6REGION IPANGASINANCALASIAOPOBLACION EAST551700024363124515476005310003A, 0003B, 0004A, 0005A, 0005B, 0006ACALASIAO CENTRAL ELEMENTARY SCHOOL05/09/2016 19:44:07
7REGION IPANGASINANCALASIAOPOBLACION EAST551700014093317411875204710001A, 0001B, 0002A, 0002BCALASIAO CENTRAL ELEMENTARY SCHOOL05/09/2016 19:49:53
8REGION IPANGASINANCALASIAOMANCUP5517005645848259183147506500132A, 0132B, 0133A, 0133BMANCUP ELEMENTARY SCHOOL05/09/2016 18:37:16
9REGION IPANGASINANCALASIAOMANCUP551700572998525219076926140133C, 0134A, 0134B, 0134C, 0135AMANCUP ELEMENTARY SCHOOL05/09/2016 17:51:39

Last rows

regionprovincemunicipalitybarangayclustered_precinctcayetanoescuderohonasanmarcosrobredotrillanesregistered_votersballots_castprecinctspolling_centertimestamp
90632REGION IXZAMBOANGA DEL NORTEMUTIATINGLAN72080013644083013054584030025A, 0026A, 0026BTINGLAN ELEMENTARY SCHOOL05/10/2016 00:12:47
90633REGION IXZAMBOANGA DEL NORTEMUTIAALVENDA72080003932992318545564810006A, 0006B, 0007A, 0007BALVENDA ELEMENTARY SCHOOL05/10/2016 02:23:07
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90639REGION IXZAMBOANGA DEL NORTEMUTIASANTO TOMAS720800181005178221416285290035A, 0035B, 0036A, 0036BMUTIA NATIONAL HIGH SCHOOL05/09/2016 22:31:13
90640REGION IXZAMBOANGA DEL NORTEMUTIASANTO TOMAS720800171013625916375324740032A, 0033A, 0034AMUTIA NATIONAL HIGH SCHOOL05/10/2016 23:11:07
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